Meet the scenario, the roadmap, and the nine skills you'll walk away with after building a complete RAG system from scratch.

Discover why ChatGPT makes things up and learn when to use prompt engineering, RAG, or fine-tuning for your use case.

Design system prompts that control AI behavior — including guardrails, refusal patterns, and structured output templates that enforce reliability.

Learn how embedding models turn text into meaning-numbers so your AI finds the right document even when users use different words.

Build the knowledge infrastructure — vector storage, document chunking strategies, and metadata filtering for precise semantic search at scale.

Wire the complete retrieval-augmented generation pipeline end to end and trace a user query through every stage from input to cited response.

Optimize retrieval with re-ranking, hybrid search, and context window management so your AI assembles the best possible answer every time.

Classify the three types of AI hallucination, diagnose their root causes, and build a five-layer prevention stack to stop them.

Build evaluation datasets, measure retrieval and answer quality with real metrics, and engineer user trust through citations and transparency.

Take your RAG system to production with knowledge update pipelines, monitoring dashboards, cost management, and incident response planning.

Add access controls, audit logging, and compliance infrastructure — then scale across domains, tenants, and languages responsibly.